In a conventional vehicle insurance claim settlement scenario, insurance companies need to dispatch professional survey and damage assessment personnel to an accident site to conduct an on-site survey and damage assessment, provide a vehicle repair scheme and a compensation amount, take on-site photos, and file the damage assessment photos for a background verifier to verify the damage and the compensation amount. As the survey and damage assessment need to be conducted manually, the insurance companies need a large investment for labor costs and specialized knowledge training costs. In terms of the experience of ordinary users, as the users need to wait for a manual surveyor to take photos on site, a damage assessor to assess damage on the repair site, and a damage verifier to conduct a background damage check during the claim settlement process, the claim settlement cycle takes up to 1-3 days, the users' waiting time is relatively long, and the experience is poor.
For the industry pain point of huge labor costs mentioned in the background, some people started to conceive of applying artificial intelligence and machine learning to vehicle damage assessment scenarios, hoping that vehicle damage situations reflected in photos can be automatically identified based on on-site damage photos photographed by general users and using computer vision image identification technologies in the artificial intelligence field, and moreover, repair schemes can be automatically provided. In this way, there is no need to conduct manual surveys, damage assessments, and damage verification, greatly reducing insurance company costs and improving the vehicle insurance claim settlement experience of ordinary users.
However, the accuracy of the current smart damage assessment schemes in determining vehicle damage needs to be further improved. Therefore, it is desirable to have an improved scheme, which can further optimize vehicle damage detection results and improve identification accuracy.